We put forth an unsupervised, non-parametric, sliding-window based algorithm that builds on the work done by [1]} to generate our sequential mistrust scores \(s_\text{mis}\) . We consider a sequence of \(r\)  samples in the input data stream denoted by \( \lbrace  x_1,x_2,...,x_t,...,x_r \rbrace \) . We obtain the scalar latent-space mistrust scores for the \(r\)  samples, \( \lbrace  s_\text{LSS}(x_1), s_\text{LSS}(x_2),...,s_\text{LSS}(x_t),...,s_\text{LSS}(x_r) \rbrace \)  from their representations \(\lbrace  \textbf {h}(x_1),\textbf {h}(x_2),...,\textbf {h}(x_t),...,\textbf {h}(x_r) \rbrace \)  as explained in Section REF .
